code: def add_data_to_errors_df(df, error_name, point, errors):
      more_data = pd.DataFrame(
          {
              "POINT": point,
              "RIDGE": errors["RIDGE_" + error_name],
              "RIDGE_NEMO": errors["RIDGE_NEMO_" + error_name],
              "ARIMA": errors["ARIMA_" + error_name],
              "ARIMA_NEMO": errors["ARIMA_NEMO_" + error_name],
              "STL_ARIMA": errors["STL_ARIMA_" + error_name],
              "STL_ARIMA_NEMO": errors["STL_ARIMA_NEMO_" + error_name],
          },
          index=[0],
      )
      df = pd.concat([df, more_data], ignore_index=True)
      return df
  
imports: import os
  from copy import deepcopy
  import numpy
  import pandas
  from matplotlib import pyplot
  from sklearn.metrics import mean_squared_error, mean_absolute_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TsForecastingParams, TaskTypesEnum
label_desc: Use image node_id 14 for calling a global function with example usage: add_data_to_errors_df(df, error_name, point, errors) and returns: df